Towards Interactive Recommender Systems with the Doctor-in-the-Loop

Abstract

Recommender Systems are a perfect example for automatic Machine Learning (aML) – which is the fastest growing field in computer science generally and health informatics specifically. The general goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions and decision support – which is of the central interest of health informatics. Whilst automatic approaches greatly benefit from big data with many training sets, in the health domain experts are often confronted with a small number of complex data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive Machine Learning (iML) may be of help, which can be defined as “algorithms that can interact with agents and can optimize their learning behaviour through these interactions, where the agents can also be human”. Such a human can be an expert, i.e. a medical doctor, and this “doctor-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human expert agent involved in the learning phase. Important future research aspects are in the combined use of both human intelligence and computer intelligence, in the context of hybrid multi-agent recommender systems which can also make use of the power of crowdsourcing to make use of joint decision making – which can be very helpful e.g. in the diagnosis and treatment of rare diseases.